\chapter{SERIMI: Class-based Matching for Instance Matching Across Heterogeneous Datasets} % Write in your own chapter title
\label{chapter:serimi}
\lhead{Chapter 3. \emph{SERIMI: Class-based Matching for Instance Matching Across Heterogeneous Datasets}} % Write in your own chapter title to set the page header
\begin{abstract}  
 %We study the problem of detecting different instance representations that refer to the same real world entity, also called \emph{instance matching}. 
State-of-the-art instance matching approaches do not perform well when used for matching instances \emph{across heterogeneous datasets}. This shortcoming derives from their core operation depending on \emph{direct matching}, which involves a direct comparison of 
%instances in the 
instances in the source   with 
%instances in the 
instances in the target dataset. Direct matching  
%matching paradigm 
is not suitable when the overlap between the datasets is  small. 
%, which is often the case with heterogeneous data. 
%to provide sufficient cues for a direct comparison. 
We propose a new paradigm called \textit{class-based matching} to solve this problem. 
%, which we use in combination with direct matching. 
Given a class of instances from the source dataset, called the \emph{class of interest}, and a set of candidate matches retrieved from the target, 
%(via direct matching), 
class-based matching   refines the candidates by filtering out those that do not belong to the class of interest. For this refinement, only data in the target is used, i.e., no direct comparison between source and target is involved. 
%Besides the main idea, we also discuss optimizations to \emph{compactly represent the class of interest} for greater efficiency and a method to \emph{automatically select the threshold} for filtering matches more effectively. 
Based on extensive experiments using 
%ly evaluate our approach
%, called SERIMI, 
%using two 
public benchmarks, 
we show our approach greatly improves the results of state-of-the-art systems, especially on difficult matching tasks.  
% and several other state-of-the-art systems not covered by the benchmarks. The results suggest that SERIMI uses valuable 
%These \emph{extensive experiments} show that SERIMI yields superior results. The class-based matching achieved competitive results when compared to the direct matching; and most importantly, it was complementary to it when the direct matching presented a low performance. In average, SERIMI outperformed all baselines.  \todo{i added more about results. not number because they are not so impressive.}
\end{abstract}  
\pagebreak

\input{./Chapters/Chapter3/sec-introduction}
\input{./Chapters/Chapter3/sec-definitions}
\input{./Chapters/Chapter3/sec-overview-directmatching}
\input{./Chapters/Chapter3/sec-overview-classbasedmatching} 
%\input{./Chapters/Chapter3/sec-overview}
\input{./Chapters/Chapter3/sec-approach}
\input{./Chapters/Chapter3/sec-reduction}
\input{./Chapters/Chapter3/sec-threshold}
\input{./Chapters/Chapter3/sec-evaluation1} 
\input{./Chapters/Chapter3/sec-evaluation2}
\input{./Chapters/Chapter3/sec-related}

\section{Conclusion}
 \label{chapter:serimi8}

In this work, we propose an unsupervised instance matching approach that combines direct-based matching with a novel class-based matching technique to infer Sameas relation over heterogeneous data. 
%This method focuses on determining similarity between instances, specially when there is not enough overlapping among source and target instances. Also, we propose an efficient class-based matching algorithm and a method that uses a statistic outlier detection strategy to eliminate false positive matches from a set of candidates matches. 
We evaluated our method using two public benchmarks: OAEI 2010 and 2011. The results show that we achieved good and competitive results compared to representative systems focused on instance matching over heterogeneous data.
 
 
%\input{./Chapters/Chapter3/sec-appendix}
 


